Vioxx redux

Simulated Docking of Zanamivir with the 2009 Pandemic Strain Influenza A/H1N1 Neuraminidase Active Site Abstract Influenza neuraminidases are glycoproteins that facilitate the transmission of the influenza virus from cell to cell. Zanamivir is a widely used neuraminidase inhibitor. Here I provide a computational docking analysis of zanamivir with the active site of the neuraminidase of the 2009 Influenza A/H1N1 strain. The computed inhibitor/receptor binding energy suggests that zanamivir would be only marginally effective against that strain. Keywords: Influenza, H1N1, neuraminidase, zanamivir

1.0 Introduction
glycoproteins that facilitate the transmission
the group-1 N1, N4 and N8 neuraminidases
of the influenza virus from cell to cell.
([1]) reveal that the active sites of these
dimensional structure from that of group-2
enzymes. The differences lie in a loop of
amino acids known as the "150-loop", which
conformation that opens a cavity not present
pyran-2-carboxylic acid; [10]) is a widely
loop contains an amino acid designated Asp
151; the side chain of this amino acid has a
carboxylic acid that, in group-1 enzymes,
points away from the active site as a result
of the 'open' conformation of the 150-loop.
related sero-subtypes, and these subtypes
The side chain of another active-site amino
correspond at least roughly to differences in
conformation in group-1 enzymes compared
neuraminidases. The subtypes fall into two
with the group-2 neuraminidases (8]).
groups ([3]): group-1 contains the subtypes
acid side chains form critical interactions
with neuraminidase inhibitors. For neuraminidase subtypes with the “open
conformation” 150-loop, the side chains
is a structural description of most of the
inhibitors tightly ([8]). The active site
([10]) using AutoDock Tools v 4.2 (ADT,
docking of zanamivir to the receptor. More
outside the active site. This means that
against another with the same active-site
extracted. (3TI3 identifies the active site of
with some group 2-like features in its active
torsions in the ligand and active site were
2.0 Method
to be flexible wherever that assumption is
assess the binding energy of the active site
physically possible, was auto-docked to the
of crystallized A/California/04/2009(H1N1))
active site, assumed to be rigid, using the
Lamarckian genetic algorithm implemented
otherwise noted, all processing described in
configuration from the analysis was saved,
Inspiron 545 with an Intel Core2 Quad CPU
and the distances between the receptor and
ligand in 3TI3, and those computed here,
RAM, running under the Windows Vista
docking are shown in Figure 1. Most values are, or are a consequence of, ADT defaults.
________________________________________________________________________ autodock_parameter_version 4.2 # used by autodock to validate parameter set outlev 1 # diagnostic output level intelec # calculate internal electrostatics seed pid time # seeds for random generator ligand_types C HD OA N # atoms types in ligand fld 3TI3_active.maps.fld # grid_data_file map 3TI3_active.C.map # atom-specific affinity map map 3TI3_active.HD.map # atom-specific affinity map map 3TI3_active.OA.map # atom-specific affinity map map 3TI3_active.N.map # atom-specific affinity map elecmap 3TI3_active.e.map # electrostatics map desolvmap 3TI3_active.d.map # desolvation map move zanamivir.pdbqt # small molecule about -29.5772 12.7517 -20.6465 # small molecule center tran0 random # initial coordinates/A or random axisangle0 random # initial orientation dihe0 random # initial dihedrals (relative) or random tstep 2.0 # translation step/A qstep 50.0 # quaternion step/deg dstep 50.0 # torsion step/deg torsdof 9 # torsional degrees of freedom rmstol 2.0 # cluster_tolerance/A extnrg 1000.0 # external grid energy e0max 0.0 10000 # max initial energy; max number of retries ga_pop_size 150 # number of individuals in population ga_num_evals 2500000 # maximum number of energy evaluations ga_num_generations 27000 # maximum number of generations ga_elitism 1 # number of top individuals to survive to next generation ga_mutation_rate 0.02 # rate of gene mutation ga_crossover_rate 0.8 # rate of crossover ga_window_size 10 # ga_cauchy_alpha 0.0 # Alpha parameter of Cauchy distribution ga_cauchy_beta 1.0 # Beta parameter Cauchy distribution set_ga # set the above parameters for GA or LGA sw_max_its 300 # iterations of Solis & Wets local search sw_max_succ 4 # consecutive successes before changing rho sw_max_fail 4 # consecutive failures before changing rho sw_rho 1.0 # size of local search space to sample sw_lb_rho 0.01 # lower bound on rho ls_search_freq 0.06 # probability of performing local search on individual set_psw1 # set the above pseudo-Solis & Wets parameters unbound_model bound # state of unbound ligand ga_run 10 # do this many hybrid GA-LS runs analysis # perform a ranked cluster analysis

Figure 3 is a rendering of the active-site/inhibitor configuration computed in this study. Figure 3. Rendering of zanamivir computationally docked with the active site of PDB 3TI3. The molecular surface of the receptor is shown in white; the inhibitor, in stick form in grey. Only the interior, inhibitor-containing region of the molecular surface of the active site can be compared to in situ data: the surface distal to the interior is a computational artifact, generated by the assumption that active site is detached from the rest of the receptor. ______________________________________________________________________________ The distances between ligand and receptor
however, be more effective than oseltamivir
distances in the present computation were
assumes that the receptor is rigid. This
assumption is appropriate for the binding
4.0 Discussion
However, the calculation does not reflect
what receptor "flexing" could contribute to
The method described in Section 2.0 and the
the interaction of the ligand with native
Sections 2.0 and 3.0 assumes receptor is in a
computed in this study (~408 nanoMolar at
crystallized form. In situ, at physiologically
~298 K) is comparable inhibition constant of
normal temperatures (~310 K), the receptor
clinically effective ([10], [11], [13], [14],
[15]) against several H1N1 genotypes. This
therefore, may not be identical to their
genetic algorithm used in this work could be
[5] Butler D. Avian flu special: The flu
models could be applied to this problem, and
pandemic: were we ready? Nature 435 (26
its own active site. The work described in
this paper was performed on Chain A only.
highly similar to the Chain A active site.
Future work will assess the ligand/receptor
Summary: Interim Recommendations for the Use of Influenza Antiviral Medications in the Setting of Oseltamivir Resistance among Circulating Influenza A (H1N1) Viruses, 5.0 Acknowledgements 2008-09 Influenza Season. 19 December 2008.
This work benefited from discussions with
[8] Luo M. Structural biology: antiviral
drugs fit for a purpose. Nature 443 (7
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